The field of prognostics, as applied to the operation of complex equipment, relates to a process for estimating the remaining life of a component. Remaining life estimates provide indispensable information for the operation of this equipment. They also provide decision-making aids that allow operators to change operational characteristics (e.g., load) which, in turn, may prolong the life of the component. In addition, they further allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted equipment to fully functioning equipment. Examples of the types of equipment that are amenable to prognostics include aircraft engines (e.g., military and commercial), medical equipment, and power plants, to name a few. It also allows planner to optimize the use of their assets. Furthermore, this will help to guarantee mission success. Missions are here understood either as military missions or, in a civilian context, as operations involving the fulfillment of objectives involving equipment (such as aircraft).
Reasoners attempt to analyze a variety of information sources toward achieving a particular goal. In the case of complex equipment, e.g., one goal of the reasoner is to provide a remaining life estimate. To that end, it negotiates and aggregates data from independent information sources while taking their inherent uncertainty into account. The uncertainty may vary as a function of time, the priors on reliability of the information sources, and domain knowledge, among others. In addition, the reasoner needs to be ensured that the information that is being used provides the maximum information content. There are a number of issues that need to be dealt with prior to the actual aggregation. Specifically, the information needs to be checked for consistency, and it needs to be cleaned of outliers, noise, faulty or otherwise bad sensor information. It should also be conditioned and formatted to allow a proper comparison. These, and other considerations, must be taken into account by the reasoner.
By way of example, prognostics activities performed with respect to bearing components utilize sensor information that provides feedback on current damage levels. During operation, initially localized spall may grow and ultimately result in loss of function. Factors affecting damage initiation and damage propagation include changes in bearing loads, speeds, and environment. Lubrication, presence of material defects, surface degradation, and external contamination all factor into the bearing environment. Subsurface fatigue cracks are induced at locations of peak shear stress, become surface-connected, and lead to eventual liberation of material. Thus, one approach to bearing prognosis is to assess the micro-structural evolution, environmental embrittlement, cyclic hardening, and residual stress in order to calculate the propagation of bearing damage. The current state of the bearing, e.g., condition of health, may be determined by feeding direct sensor data and indirect parameters computed from sensor data into an ensemble of diagnostic algorithms as a basis for input to, e.g., fault-evolution and life models. The algorithms arrive at their conclusion either by direct measurement, models supported by measurements, or are simply triggered by measurements. The information sources that the reasoner relies on may be updated at different intervals during or between flights and may have different prediction horizons.
Ideally, features derived from sensor measurements would have monotonically changing properties that accurately reflect increasing component damage and be provided irrespective of external conditions. However, in practice this is nearly never the case: features reflect the noise inherent in sensed data and react differently during particular stages of damage evolution (e.g., some are useful for fault detection, but not for damage growth tracking).
Oil debris monitor features, such as particle counts, have excellent tracking properties that are robust to changes of environmental parameters. However, they may be not as suitable to identification of fault initiation because their resolution is too low for detection of small damage levels. In addition, absolute counts can be misleading when material gets trapped over time and due to external contamination. Better sensors for fault initiation and initial fault growth tracking may be vibration sensors that have the promise to pick up smaller damage levels. Features from various transforms such as Fourier, Hilbert, and wavelets can be useful in detecting and categorizing incipient faults. However, the vibration sensor's capacity for early detection comes at the price of sensitivity to environmental effects that are sometimes difficult to quantify or correct. For example, in an aircraft engine (and in particular, one that is under conditions of military use), these changes can be significant. Thus, it may be expedient to aggregate vibration and oil debris information to take advantage of the benefits of both. The fusion of information from oil debris and vibration sources, along with knowledge about system and machinery history can result in interactions that may improve the confidence about system condition.
The field of prognostics is reliant on diagnostics to provide a trigger point for the prognostic algorithms. That is, no prognostic estimates are calculated before diagnostics has detected a fault condition. In the absence of abnormal conditions, or fault conditions, the best estimates for remaining component life are often fleet wide statistics expressed by Weibull curves or other suitable mechanism. Condition-based systems depend on reliable fault diagnostics to initiate the prognostic algorithms. It is therefore important to optimize the diagnostic capability to attain optimal prognostics. If diagnostics recognizes the start point of damage too late, the damage propagation models will always lag reality and keep underestimating the damage. If prognostic algorithms are initiated when there is no real damage, the benefit of true remaining life estimate is erased.
The remaining useful life (RUL) estimates are typically in units of time until the likelihood of failure reaches a particular threshold. RUL is often estimated indirectly via the calculation of a metric that, when exceeding a particular threshold, indicates imminent component failure. In the context of bearing race spall, this metric could be spall length. When spall length surpasses a critical value, damage accumulates rapidly; bearing cage failure occurs soon after this threshold has been exceeded.
The utility of future estimates is in direct proportion to the amount of associated uncertainty. That is, if an estimate has very large confidence bounds, the utility of such an estimate becomes very small because an operator would have to make decisions to repair components at an otherwise acceptable level of risk. A key contribution of the reasoner is to assess the uncertainty of the individual estimators and to aggregate them such that the uncertainty bounds of the resulting output are smaller than any of the individual information sources alone. Moreover, the output of the reasoner is more accurate than any individual information source alone.
Several fundamentally different approaches may be employed to estimate future damage. One is to model from first principles the physics of the system as well as the fault propagation for given load and speed conditions. Such a model must include detailed knowledge of material properties, thermodynamic behavior, etc. Alternatively, an empirical (also referred to as experience-based) model can be employed wherein data from experiments at known conditions and component damage level are used to build a model for fault propagation rate. Such a model relies heavily on a reasonably large set of experiments that sufficiently explores the operating space.
The two approaches mentioned for estimating future damage have various advantages and disadvantages. The physics-based model relies on the assumption that the fault mode modeled using the specific geometry, material properties, temperature, load, and speed conditions will be similar to the actual fault mode. Deviation in any of those parameters will likely result in an error that is amplified over time. In contrast, the experience-based model assumes that the data available sufficiently maps the space and that interpolations (and small extrapolations) from that map can accurately estimate the fault rate.
What is needed, therefore, is a way to provide real-time (or near real-time) information concerning existing and future asset health that is more accurate and reliable than existing processes.